def step(self, epoch=None):
current_lr = self.get_lr()[0]
# Outside of warmup epochs, we use the same learning rate for every step
# in an epoch. Don't bother updating learning rate if it hasn't changed.
if abs(current_lr - self._previous_lr) >= self._min_delta_to_update_lr:
super(WarmupAndExponentialDecayScheduler, self).step()
self._previous_lr = current_lr
else:
self._step_count += 1 # This normally happens in super().step().
# Add current learning rate to Tensorboard metrics. For warmup epochs,
# log the learning rate at every step. For non-warmup epochs, log only
# the first step since the entire epoch will use the same learning rate.
if self._summary_writer:
if self._epoch() < self._num_warmup_epochs or (
self._step_count % self._num_steps_per_epoch == 0):
test_utils.add_scalar_to_summary(
self._summary_writer, 'LearningRate',
self.optimizer.param_groups[0]['lr'], self._step_count)
def train_imagenet():
print('==> Preparing data..')
img_dim = get_model_property('img_dim')
if FLAGS.fake_data:
train_dataset_len = 1200000 # Roughly the size of Imagenet dataset.
train_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 3, img_dim, img_dim),
torch.zeros(FLAGS.batch_size, dtype=torch.int64)),
sample_count=train_dataset_len // FLAGS.batch_size //
xm.xrt_world_size())
test_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.test_set_batch_size, 3, img_dim, img_dim),
torch.zeros(FLAGS.test_set_batch_size, dtype=torch.int64)),
sample_count=50000 // FLAGS.batch_size // xm.xrt_world_size())
else:
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = torchvision.datasets.ImageFolder(
os.path.join(FLAGS.datadir, 'train'),
transforms.Compose([
transforms.RandomResizedCrop(img_dim),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
train_dataset_len = len(train_dataset.imgs)
resize_dim = max(img_dim, 256)
test_dataset = torchvision.datasets.ImageFolder(
os.path.join(FLAGS.datadir, 'val'),
# Matches Torchvision's eval transforms except Torchvision uses size
# 256 resize for all models both here and in the train loader. Their
# version crashes during training on 299x299 images, e.g. inception.
transforms.Compose([
transforms.Resize(resize_dim),
transforms.CenterCrop(img_dim),
transforms.ToTensor(),
normalize,
]))
train_sampler = None
if xm.xrt_world_size() > 1:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=True)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=FLAGS.batch_size,
sampler=train_sampler,
drop_last=FLAGS.drop_last,
shuffle=False if train_sampler else True,
num_workers=FLAGS.num_workers)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=FLAGS.test_set_batch_size,
drop_last=FLAGS.drop_last,
shuffle=False,
num_workers=FLAGS.num_workers)
torch.manual_seed(42)
device = xm.xla_device()
model = get_model_property('model_fn')().to(device)
writer = None
if xm.is_master_ordinal():
writer = test_utils.get_summary_writer(FLAGS.logdir)
optimizer = optim.SGD(model.parameters(),
lr=FLAGS.lr,
momentum=FLAGS.momentum,
weight_decay=1e-4)
num_training_steps_per_epoch = train_dataset_len // (FLAGS.batch_size *
xm.xrt_world_size())
lr_scheduler = schedulers.wrap_optimizer_with_scheduler(
optimizer,
scheduler_type=getattr(FLAGS, 'lr_scheduler_type', None),
scheduler_divisor=getattr(FLAGS, 'lr_scheduler_divisor', None),
scheduler_divide_every_n_epochs=getattr(
FLAGS, 'lr_scheduler_divide_every_n_epochs', None),
num_steps_per_epoch=num_training_steps_per_epoch,
summary_writer=writer)
loss_fn = nn.CrossEntropyLoss()
def train_loop_fn(loader):
tracker = xm.RateTracker()
model.train()
for x, (data, target) in enumerate(loader):
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss.backward()
xm.optimizer_step(optimizer)
tracker.add(FLAGS.batch_size)
if lr_scheduler:
lr_scheduler.step()
if x % FLAGS.log_steps == 0:
test_utils.print_training_update(device, x, loss.item(),
tracker.rate(),
tracker.global_rate())
def test_loop_fn(loader):
total_samples = 0
correct = 0
model.eval()
for data, target in loader:
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
accuracy = 100.0 * correct / total_samples
test_utils.print_test_update(device, accuracy)
return accuracy
accuracy = 0.0
for epoch in range(1, FLAGS.num_epochs + 1):
para_loader = pl.ParallelLoader(train_loader, [device])
train_loop_fn(para_loader.per_device_loader(device))
xm.master_print("Finished training epoch {}".format(epoch))
para_loader = pl.ParallelLoader(test_loader, [device])
accuracy = test_loop_fn(para_loader.per_device_loader(device))
test_utils.add_scalar_to_summary(writer, 'Accuracy/test', accuracy,
epoch)
if FLAGS.metrics_debug:
print(met.metrics_report())
test_utils.close_summary_writer(writer)
return accuracy
def train_imagenet():
print('==> Preparing data..')
img_dim = get_model_property('img_dim')
if FLAGS.fake_data:
train_dataset_len = 1200000 # Roughly the size of Imagenet dataset.
train_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 3, img_dim, img_dim),
torch.zeros(FLAGS.batch_size, dtype=torch.int64)),
sample_count=train_dataset_len // FLAGS.batch_size //
xm.xrt_world_size())
test_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.test_set_batch_size, 3, img_dim, img_dim),
torch.zeros(FLAGS.test_set_batch_size, dtype=torch.int64)),
sample_count=50000 // FLAGS.batch_size // xm.xrt_world_size())
else:
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = torchvision.datasets.ImageFolder(
os.path.join(FLAGS.datadir, 'train'),
transforms.Compose([
transforms.RandomResizedCrop(img_dim),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
train_dataset_len = len(train_dataset.imgs)
resize_dim = max(img_dim, 256)
test_dataset = torchvision.datasets.ImageFolder(
os.path.join(FLAGS.datadir, 'val'),
# Matches Torchvision's eval transforms except Torchvision uses size
# 256 resize for all models both here and in the train loader. Their
# version crashes during training on 299x299 images, e.g. inception.
transforms.Compose([
transforms.Resize(resize_dim),
transforms.CenterCrop(img_dim),
transforms.ToTensor(),
normalize,
]))
train_sampler = None
test_sampler = None
if xm.xrt_world_size() > 1:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=True)
test_sampler = torch.utils.data.distributed.DistributedSampler(
test_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=False)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=FLAGS.batch_size,
sampler=train_sampler,
shuffle=False if train_sampler else True,
num_workers=FLAGS.num_workers)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=FLAGS.test_set_batch_size,
sampler=test_sampler,
shuffle=False,
num_workers=FLAGS.num_workers)
torch.manual_seed(42)
devices = (xm.get_xla_supported_devices(
max_devices=FLAGS.num_cores) if FLAGS.num_cores != 0 else [])
# Pass [] as device_ids to run using the PyTorch/CPU engine.
torchvision_model = get_model_property('model_fn')
model_parallel = dp.DataParallel(torchvision_model, device_ids=devices)
def train_loop_fn(model, loader, device, context):
loss_fn = nn.CrossEntropyLoss()
optimizer = context.getattr_or(
'optimizer', lambda: optim.SGD(model.parameters(),
lr=FLAGS.lr,
momentum=FLAGS.momentum,
weight_decay=5e-4))
lr_scheduler = context.getattr_or(
'lr_scheduler', lambda: schedulers.wrap_optimizer_with_scheduler(
optimizer,
scheduler_type=getattr(FLAGS, 'lr_scheduler_type', None),
scheduler_divisor=getattr(FLAGS, 'lr_scheduler_divisor', None),
scheduler_divide_every_n_epochs=getattr(
FLAGS, 'lr_scheduler_divide_every_n_epochs', None),
num_steps_per_epoch=num_training_steps_per_epoch,
summary_writer=writer if xm.is_master_ordinal() else None))
tracker = xm.RateTracker()
model.train()
for x, (data, target) in loader:
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss.backward()
xm.optimizer_step(optimizer)
tracker.add(FLAGS.batch_size)
if x % FLAGS.log_steps == 0:
test_utils.print_training_update(device, x, loss.item(),
tracker.rate(),
tracker.global_rate())
if lr_scheduler:
lr_scheduler.step()
def test_loop_fn(model, loader, device, context):
total_samples = 0
correct = 0
model.eval()
for x, (data, target) in loader:
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
accuracy = 100.0 * correct / total_samples
test_utils.print_test_update(device, accuracy)
return accuracy
accuracy = 0.0
writer = SummaryWriter(log_dir=FLAGS.logdir) if FLAGS.logdir else None
num_devices = len(
xm.xla_replication_devices(devices)) if len(devices) > 1 else 1
num_training_steps_per_epoch = train_dataset_len // (FLAGS.batch_size *
num_devices)
for epoch in range(1, FLAGS.num_epochs + 1):
model_parallel(train_loop_fn, train_loader)
accuracies = model_parallel(test_loop_fn, test_loader)
accuracy = mean(accuracies)
print('Epoch: {}, Mean Accuracy: {:.2f}%'.format(epoch, accuracy))
global_step = (epoch - 1) * num_training_steps_per_epoch
test_utils.add_scalar_to_summary(writer, 'Accuracy/test', accuracy,
global_step)
if FLAGS.metrics_debug:
print(torch_xla._XLAC._xla_metrics_report())
return accuracy
def train_imagenet():
print("==> Preparing data..")
img_dim = get_model_property("img_dim")
if FLAGS.fake_data:
train_dataset_len = 1200000 # Roughly the size of Imagenet dataset.
train_loader = xu.SampleGenerator(
data=(
torch.zeros(FLAGS.batch_size, 3, img_dim, img_dim),
torch.zeros(FLAGS.batch_size, dtype=torch.int64),
),
sample_count=train_dataset_len // FLAGS.batch_size // xm.xrt_world_size(),
)
test_loader = xu.SampleGenerator(
data=(
torch.zeros(FLAGS.test_set_batch_size, 3, img_dim, img_dim),
torch.zeros(FLAGS.test_set_batch_size, dtype=torch.int64),
),
sample_count=50000 // FLAGS.batch_size // xm.xrt_world_size(),
)
else:
normalize = transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
train_dataset = torchvision.datasets.ImageFolder(
os.path.join(FLAGS.datadir, "train"),
transforms.Compose(
[
transforms.RandomResizedCrop(img_dim),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]
),
)
train_dataset_len = len(train_dataset.imgs)
resize_dim = max(img_dim, 256)
test_dataset = torchvision.datasets.ImageFolder(
os.path.join(FLAGS.datadir, "val"),
# Matches Torchvision's eval transforms except Torchvision uses size
# 256 resize for all models both here and in the train loader. Their
# version crashes during training on 299x299 images, e.g. inception.
transforms.Compose(
[
transforms.Resize(resize_dim),
transforms.CenterCrop(img_dim),
transforms.ToTensor(),
normalize,
]
),
)
train_sampler = None
test_sampler = None
if xm.xrt_world_size() > 1:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=True,
)
test_sampler = torch.utils.data.distributed.DistributedSampler(
test_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=False,
)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=FLAGS.batch_size,
sampler=train_sampler,
drop_last=True,
shuffle=False if train_sampler else True,
num_workers=FLAGS.num_workers,
)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=FLAGS.test_set_batch_size,
sampler=test_sampler,
drop_last=False,
shuffle=False,
num_workers=FLAGS.num_workers,
)
torch.manual_seed(42)
devices = (
xm.get_xla_supported_devices(max_devices=FLAGS.num_cores)
if FLAGS.num_cores != 0
else ["cpu"]
)
print("use tpu devices", devices)
# Pass [] as device_ids to run using the PyTorch/CPU engine.
torchvision_model = get_model_property("model_fn")
model_parallel = dp.DataParallel(torchvision_model, device_ids=devices)
def train_loop_fn(model, loader, device, context):
loss_fn = nn.CrossEntropyLoss()
optimizer = context.getattr_or(
"optimizer",
lambda: optim.SGD(
model.parameters(),
lr=FLAGS.lr,
momentum=FLAGS.momentum,
weight_decay=1e-4,
),
)
lr_scheduler = context.getattr_or(
"lr_scheduler",
lambda: schedulers.wrap_optimizer_with_scheduler(
optimizer,
scheduler_type=getattr(FLAGS, "lr_scheduler_type", None),
scheduler_divisor=getattr(FLAGS, "lr_scheduler_divisor", None),
scheduler_divide_every_n_epochs=getattr(
FLAGS, "lr_scheduler_divide_every_n_epochs", None
),
num_steps_per_epoch=num_training_steps_per_epoch,
summary_writer=writer if xm.is_master_ordinal() else None,
),
)
tracker = xm.RateTracker()
model.train()
total_samples = 0
correct = 0
top5_accuracys = 0
losses = 0
for x, (data, target) in loader:
optimizer.zero_grad()
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum()
total_samples += data.size()[0]
top5_accuracys += topk_accuracy(output, target, topk=5)
loss = loss_fn(output, target)
loss.backward()
losses += loss.item()
xm.optimizer_step(optimizer)
tracker.add(FLAGS.batch_size)
if x % FLAGS.log_steps == 0:
print(
"[{}]({}) Loss={:.5f} Top-1 ACC = {:.2f} Rate={:.2f} GlobalRate={:.2f} Time={}".format(
str(device),
x,
loss.item(),
(100.0 * correct / total_samples).item(),
tracker.rate(),
tracker.global_rate(),
time.asctime(),
)
)
if lr_scheduler:
lr_scheduler.step()
return (
losses / (x + 1),
(100.0 * correct / total_samples).item(),
(top5_accuracys / (x + 1)).item(),
)
def test_loop_fn(model, loader, device, context):
total_samples = 0
correct = 0
top5_accuracys = 0
model.eval()
for x, (data, target) in loader:
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
top5_accuracys += topk_accuracy(output, target, topk=5).item()
accuracy = 100.0 * correct / total_samples
test_utils.print_test_update(device, accuracy)
return accuracy, top5_accuracys
accuracy = 0.0
writer = SummaryWriter(FLAGS.logdir) if FLAGS.logdir else None
num_devices = len(xm.xla_replication_devices(devices)) if len(devices) > 1 else 1
num_training_steps_per_epoch = train_dataset_len // (FLAGS.batch_size * num_devices)
max_accuracy = 0.0
print("train_loader_len", len(train_loader), num_training_steps_per_epoch)
print("test_loader_len", len(test_loader))
for epoch in range(1, FLAGS.num_epochs + 1):
global_step = (epoch - 1) * num_training_steps_per_epoch
# Train evaluate
metrics = model_parallel(train_loop_fn, train_loader)
losses, accuracies, top5_accuracys = zip(*metrics)
loss = mean(losses)
accuracy = mean(accuracies)
top5_accuracy = mean(top5_accuracys)
print(
"Epoch: {} (Train), Loss {}, Mean Top-1 Accuracy: {:.2f} Top-5 accuracy: {}".format(
epoch, loss, accuracy, top5_accuracy
)
)
test_utils.add_scalar_to_summary(writer, "Loss/train", loss, global_step)
test_utils.add_scalar_to_summary(
writer, "Top-1 Accuracy/train", accuracy, global_step
)
test_utils.add_scalar_to_summary(
writer, "Top-5 Accuracy/train", top5_accuracy, global_step
)
# Test evaluate
metrics = model_parallel(test_loop_fn, test_loader)
accuracies, top5_accuracys = zip(*metrics)
top5_accuracys = sum(top5_accuracys)
top5_accuracy = top5_accuracys / len(test_loader)
accuracy = mean(accuracies)
print(
"Epoch: {} (Valid), Mean Top-1 Accuracy: {:.2f} Top-5 accuracy: {}".format(
epoch, accuracy, top5_accuracy
)
)
test_utils.add_scalar_to_summary(
writer, "Top-1 Accuracy/test", accuracy, global_step
)
test_utils.add_scalar_to_summary(
writer, "Top-5 Accuracy/test", top5_accuracy, global_step
)
if FLAGS.metrics_debug:
print(met.metrics_report())
if accuracy > max_accuracy:
max_accuracy = max(accuracy, max_accuracy)
torch.save(
model_parallel.models[0].to("cpu").state_dict(),
f"./reports/resnet50_model-{epoch}.pt",
)
model_parallel.models[0].to(devices[0])
test_utils.close_summary_writer(writer)
print("Max Accuracy: {:.2f}%".format(accuracy))
return max_accuracy
def train_imagenet():
torch.manual_seed(42)
device = xm.xla_device()
# model = get_model_property('model_fn')().to(device)
model = create_model(
FLAGS.model,
pretrained=FLAGS.pretrained,
num_classes=FLAGS.num_classes,
drop_rate=FLAGS.drop,
global_pool=FLAGS.gp,
bn_tf=FLAGS.bn_tf,
bn_momentum=FLAGS.bn_momentum,
bn_eps=FLAGS.bn_eps,
drop_connect_rate=0.2,
checkpoint_path=FLAGS.initial_checkpoint,
args = FLAGS).to(device)
model_ema=None
if FLAGS.model_ema:
# Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
# import pdb; pdb.set_trace()
model_e = create_model(
FLAGS.model,
pretrained=FLAGS.pretrained,
num_classes=FLAGS.num_classes,
drop_rate=FLAGS.drop,
global_pool=FLAGS.gp,
bn_tf=FLAGS.bn_tf,
bn_momentum=FLAGS.bn_momentum,
bn_eps=FLAGS.bn_eps,
drop_connect_rate=0.2,
checkpoint_path=FLAGS.initial_checkpoint,
args = FLAGS).to(device)
model_ema = ModelEma(
model_e,
decay=FLAGS.model_ema_decay,
device='cpu' if FLAGS.model_ema_force_cpu else '',
resume=FLAGS.resume)
print('==> Preparing data..')
img_dim = 224
if FLAGS.fake_data:
train_dataset_len = 1200000 # Roughly the size of Imagenet dataset.
train_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 3, img_dim, img_dim),
torch.zeros(FLAGS.batch_size, dtype=torch.int64)),
sample_count=train_dataset_len // FLAGS.batch_size //
xm.xrt_world_size())
test_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 3, img_dim, img_dim),
torch.zeros(FLAGS.batch_size, dtype=torch.int64)),
sample_count=50000 // FLAGS.batch_size // xm.xrt_world_size())
# else:
# normalize = transforms.Normalize(
# mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
# train_dataset = torchvision.datasets.ImageFolder(
# os.path.join(FLAGS.data, 'train'),
# transforms.Compose([
# transforms.RandomResizedCrop(img_dim),
# transforms.RandomHorizontalFlip(),
# transforms.ToTensor(),
# normalize,
# ]))
# train_dataset_len = len(train_dataset.imgs)
# resize_dim = max(img_dim, 256)
# test_dataset = torchvision.datasets.ImageFolder(
# os.path.join(FLAGS.data, 'val'),
# # Matches Torchvision's eval transforms except Torchvision uses size
# # 256 resize for all models both here and in the train loader. Their
# # version crashes during training on 299x299 images, e.g. inception.
# transforms.Compose([
# transforms.Resize(resize_dim),
# transforms.CenterCrop(img_dim),
# transforms.ToTensor(),
# normalize,
# ]))
# train_sampler = None
# if xm.xrt_world_size() > 1:
# train_sampler = torch.utils.data.distributed.DistributedSampler(
# train_dataset,
# num_replicas=xm.xrt_world_size(),
# rank=xm.get_ordinal(),
# shuffle=True)
# train_loader = torch.utils.data.DataLoader(
# train_dataset,
# batch_size=FLAGS.batch_size,
# sampler=train_sampler,
# shuffle=False if train_sampler else True,
# num_workers=FLAGS.workers)
# test_loader = torch.utils.data.DataLoader(
# test_dataset,
# batch_size=FLAGS.batch_size,
# shuffle=False,
# num_workers=FLAGS.workers)
else:
train_dir = os.path.join(FLAGS.data, 'train')
data_config = resolve_data_config(model, FLAGS, verbose=FLAGS.local_rank == 0)
dataset_train = Dataset(train_dir)
collate_fn = None
if not FLAGS.no_prefetcher and FLAGS.mixup > 0:
collate_fn = FastCollateMixup(FLAGS.mixup, FLAGS.smoothing, FLAGS.num_classes)
train_loader = create_loader(
dataset_train,
input_size=data_config['input_size'],
batch_size=FLAGS.batch_size,
is_training=True,
use_prefetcher=not FLAGS.no_prefetcher,
rand_erase_prob=FLAGS.reprob,
rand_erase_mode=FLAGS.remode,
interpolation='bicubic', # FIXME cleanly resolve this? data_config['interpolation'],
mean=data_config['mean'],
std=data_config['std'],
num_workers=FLAGS.workers,
distributed=FLAGS.distributed,
collate_fn=collate_fn,
use_auto_aug=FLAGS.auto_augment,
use_mixcut=FLAGS.mixcut,
)
eval_dir = os.path.join(FLAGS.data, 'val')
train_dataset_len = len(train_loader)
if not os.path.isdir(eval_dir):
logging.error('Validation folder does not exist at: {}'.format(eval_dir))
exit(1)
dataset_eval = Dataset(eval_dir)
test_loader = create_loader(
dataset_eval,
input_size=data_config['input_size'],
batch_size = FLAGS.batch_size,
is_training=False,
use_prefetcher=FLAGS.prefetcher,
interpolation=data_config['interpolation'],
mean=data_config['mean'],
std=data_config['std'],
num_workers=FLAGS.workers,
distributed=FLAGS.distributed,
)
writer = None
start_epoch = 0
if FLAGS.output and xm.is_master_ordinal():
writer = SummaryWriter(log_dir=FLAGS.output)
optimizer = create_optimizer(flags, model)
lr_scheduler, num_epochs = create_scheduler(flags, optimizer)
if start_epoch > 0:
lr_scheduler.step(start_epoch)
# optimizer = optim.SGD(
# model.parameters(),
# lr=FLAGS.lr,
# momentum=FLAGS.momentum,
# weight_decay=5e-4)
num_training_steps_per_epoch = train_dataset_len // (
FLAGS.batch_size * xm.xrt_world_size())
lr_scheduler = schedulers.wrap_optimizer_with_scheduler(
optimizer,
scheduler_type=getattr(FLAGS, 'lr_scheduler_type', None),
scheduler_divisor=getattr(FLAGS, 'lr_scheduler_divisor', None),
scheduler_divide_every_n_epochs=getattr(
FLAGS, 'lr_scheduler_divide_every_n_epochs', None),
num_steps_per_epoch=num_training_steps_per_epoch,
summary_writer=writer)
train_loss_fn = LabelSmoothingCrossEntropy(smoothing=flags.smoothing)
validate_loss_fn = nn.CrossEntropyLoss()
# loss_fn = nn.CrossEntropyLoss()
def train_loop_fn(loader):
tracker = xm.RateTracker()
model.train()
for x, (data, target) in loader:
optimizer.zero_grad()
output = model(data)
loss = train_loss_fn(output, target)
loss.backward()
xm.optimizer_step(optimizer)
tracker.add(FLAGS.batch_size)
if model_ema is not None:
model_ema.update(model)
if lr_scheduler:
lr_scheduler.step()
if x % FLAGS.log_steps == 0:
test_utils.print_training_update(device, x, loss.item(), tracker.rate(),
tracker.global_rate())
def test_loop_fn(loader):
total_samples = 0
correct = 0
model.eval()
for x, (data, target) in loader:
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
accuracy = 100.0 * correct / total_samples
test_utils.print_test_update(device, accuracy)
return accuracy
def test_loop_fn_ema(loader):
total_samples = 0
correct = 0
model_ema.eval()
for x, (data, target) in loader:
output = model_ema(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
accuracy = 100.0 * correct / total_samples
test_utils.print_test_update(device, accuracy)
return accuracy
accuracy = 0.0
for epoch in range(1, FLAGS.epochs + 1):
para_loader = dp.ParallelLoader(train_loader, [device])
train_loop_fn(para_loader.per_device_loader(device))
para_loader = dp.ParallelLoader(test_loader, [device])
accuracy = test_loop_fn(para_loader.per_device_loader(device))
print('Epoch: {}, Mean Accuracy: {:.2f}%'.format(epoch, accuracy))
if model_ema is not None:
accuracy = test_loop_fn_ema(para_loader.per_device_loader(device))
print('Epoch: {}, Mean Accuracy: {:.2f}%'.format(epoch, accuracy))
test_utils.add_scalar_to_summary(writer, 'Accuracy/test', accuracy, epoch)
if FLAGS.metrics_debug:
print(torch_xla._XLAC._xla_metrics_report())
return accuracy
def train_mnist():
torch.manual_seed(1)
if FLAGS.fake_data:
train_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 1, 28, 28),
torch.zeros(FLAGS.batch_size, dtype=torch.int64)),
sample_count=60000 // FLAGS.batch_size // xm.xrt_world_size())
test_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 1, 28, 28),
torch.zeros(FLAGS.batch_size, dtype=torch.int64)),
sample_count=10000 // FLAGS.batch_size // xm.xrt_world_size())
else:
train_dataset = datasets.MNIST(FLAGS.datadir,
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ),
(0.3081, ))
]))
test_dataset = datasets.MNIST(FLAGS.datadir,
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ),
(0.3081, ))
]))
train_sampler = None
if xm.xrt_world_size() > 1:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=True)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=FLAGS.batch_size,
sampler=train_sampler,
drop_last=FLAGS.drop_last,
shuffle=False if train_sampler else True,
num_workers=FLAGS.num_workers)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=FLAGS.batch_size,
drop_last=FLAGS.drop_last,
shuffle=False,
num_workers=FLAGS.num_workers)
# Scale learning rate to num cores
lr = FLAGS.lr * xm.xrt_world_size()
device = xm.xla_device()
model = MNIST().to(device)
writer = None
if xm.is_master_ordinal():
writer = test_utils.get_summary_writer(FLAGS.logdir)
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=FLAGS.momentum)
loss_fn = nn.NLLLoss()
def train_loop_fn(loader):
tracker = xm.RateTracker()
model.train()
for x, (data, target) in enumerate(loader):
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss.backward()
xm.optimizer_step(optimizer)
tracker.add(FLAGS.batch_size)
if x % FLAGS.log_steps == 0:
test_utils.print_training_update(device, x, loss.item(),
tracker.rate(),
tracker.global_rate())
def test_loop_fn(loader):
total_samples = 0
correct = 0
model.eval()
for data, target in loader:
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
accuracy = 100.0 * correct / total_samples
test_utils.print_test_update(device, accuracy)
return accuracy
accuracy = 0.0
for epoch in range(1, FLAGS.num_epochs + 1):
para_loader = pl.ParallelLoader(train_loader, [device])
train_loop_fn(para_loader.per_device_loader(device))
xm.master_print("Finished training epoch {}".format(epoch))
para_loader = pl.ParallelLoader(test_loader, [device])
accuracy = test_loop_fn(para_loader.per_device_loader(device))
test_utils.add_scalar_to_summary(writer, 'Accuracy/test', accuracy,
epoch)
if FLAGS.metrics_debug:
print(met.metrics_report())
test_utils.close_summary_writer(writer)
return accuracy
def train_cifar():
print('==> Preparing data..')
if FLAGS.fake_data:
train_dataset_len = 50000 # Number of example in CIFAR train set.
train_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 3, 32, 32),
torch.zeros(FLAGS.batch_size, dtype=torch.int64)),
sample_count=train_dataset_len // FLAGS.batch_size //
xm.xrt_world_size())
test_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 3, 32, 32),
torch.zeros(FLAGS.batch_size, dtype=torch.int64)),
sample_count=10000 // FLAGS.batch_size // xm.xrt_world_size())
else:
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
train_dataset = torchvision.datasets.CIFAR10(root=FLAGS.datadir,
train=True,
download=True,
transform=transform_train)
train_dataset_len = len(train_dataset)
test_dataset = torchvision.datasets.CIFAR10(root=FLAGS.datadir,
train=False,
download=True,
transform=transform_test)
train_sampler = None
test_sampler = None
if xm.xrt_world_size() > 1:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=True)
test_sampler = torch.utils.data.distributed.DistributedSampler(
test_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=False)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=FLAGS.batch_size,
sampler=train_sampler,
drop_last=FLAGS.drop_last,
shuffle=False if train_sampler else True,
num_workers=FLAGS.num_workers)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=FLAGS.batch_size,
sampler=test_sampler,
drop_last=FLAGS.drop_last,
shuffle=False,
num_workers=FLAGS.num_workers)
torch.manual_seed(42)
devices = (xm.get_xla_supported_devices(
max_devices=FLAGS.num_cores) if FLAGS.num_cores != 0 else [])
# Pass [] as device_ids to run using the PyTorch/CPU engine.
model = torchvision.models.resnet18 if FLAGS.use_torchvision else ResNet18
model_parallel = dp.DataParallel(model, device_ids=devices)
def train_loop_fn(model, loader, device, context):
loss_fn = nn.CrossEntropyLoss()
optimizer = context.getattr_or(
'optimizer', lambda: optim.SGD(model.parameters(),
lr=FLAGS.lr,
momentum=FLAGS.momentum,
weight_decay=5e-4))
tracker = xm.RateTracker()
model.train()
for x, (data, target) in enumerate(loader):
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss.backward()
xm.optimizer_step(optimizer)
tracker.add(FLAGS.batch_size)
if x % FLAGS.log_steps == 0:
test_utils.print_training_update(device, x, loss.item(),
tracker.rate(),
tracker.global_rate())
def test_loop_fn(model, loader, device, context):
total_samples = 0
correct = 0
model.eval()
for data, target in loader:
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
accuracy = 100.0 * correct / total_samples
test_utils.print_test_update(device, accuracy)
return accuracy
accuracy = 0.0
writer = test_utils.get_summary_writer(FLAGS.logdir)
num_devices = len(
xm.xla_replication_devices(devices)) if len(devices) > 1 else 1
num_training_steps_per_epoch = train_dataset_len // (FLAGS.batch_size *
num_devices)
for epoch in range(1, FLAGS.num_epochs + 1):
model_parallel(train_loop_fn, train_loader)
accuracies = model_parallel(test_loop_fn, test_loader)
accuracy = mean(accuracies)
print("Epoch: {}, Mean Accuracy: {:.2f}%".format(epoch, accuracy))
global_step = (epoch - 1) * num_training_steps_per_epoch
test_utils.add_scalar_to_summary(writer, 'Accuracy/test', accuracy,
global_step)
if FLAGS.metrics_debug:
print(met.metrics_report())
test_utils.close_summary_writer(writer)
return accuracy
def train_mnist():
torch.manual_seed(1)
if FLAGS.fake_data:
train_dataset_len = 60000 # Number of images in MNIST dataset.
train_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 1, 28, 28),
torch.zeros(FLAGS.batch_size, dtype=torch.int64)),
sample_count=train_dataset_len // FLAGS.batch_size //
xm.xrt_world_size())
test_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 1, 28, 28),
torch.zeros(FLAGS.batch_size, dtype=torch.int64)),
sample_count=10000 // FLAGS.batch_size // xm.xrt_world_size())
else:
train_dataset = datasets.MNIST(FLAGS.datadir,
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ),
(0.3081, ))
]))
train_dataset_len = len(train_dataset)
test_dataset = datasets.MNIST(FLAGS.datadir,
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ),
(0.3081, ))
]))
train_sampler = None
test_sampler = None
if xm.xrt_world_size() > 1:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=True)
test_sampler = torch.utils.data.distributed.DistributedSampler(
test_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=False)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=FLAGS.batch_size,
sampler=train_sampler,
drop_last=FLAGS.drop_last,
shuffle=False if train_sampler else True,
num_workers=FLAGS.num_workers)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=FLAGS.batch_size,
sampler=test_sampler,
drop_last=FLAGS.drop_last,
shuffle=False,
num_workers=FLAGS.num_workers)
devices = (xm.get_xla_supported_devices(
max_devices=FLAGS.num_cores) if FLAGS.num_cores != 0 else [])
# Scale learning rate to num cores
lr = FLAGS.lr * max(len(devices), 1)
# Pass [] as device_ids to run using the PyTorch/CPU engine.
model_parallel = dp.DataParallel(MNIST, device_ids=devices)
def train_loop_fn(model, loader, device, context):
loss_fn = nn.NLLLoss()
optimizer = context.getattr_or(
'optimizer', lambda: optim.SGD(
model.parameters(), lr=lr, momentum=FLAGS.momentum))
tracker = xm.RateTracker()
model.train()
for x, (data, target) in enumerate(loader):
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss.backward()
xm.optimizer_step(optimizer)
tracker.add(FLAGS.batch_size)
if x % FLAGS.log_steps == 0:
test_utils.print_training_update(device, x, loss.item(),
tracker.rate(),
tracker.global_rate())
def test_loop_fn(model, loader, device, context):
total_samples = 0
correct = 0
model.eval()
for data, target in loader:
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
accuracy = 100.0 * correct / total_samples
test_utils.print_test_update(device, accuracy)
return accuracy
accuracy = 0.0
writer = test_utils.get_summary_writer(FLAGS.logdir)
num_devices = len(
xm.xla_replication_devices(devices)) if len(devices) > 1 else 1
num_training_steps_per_epoch = train_dataset_len // (FLAGS.batch_size *
num_devices)
for epoch in range(1, FLAGS.num_epochs + 1):
model_parallel(train_loop_fn, train_loader)
accuracies = model_parallel(test_loop_fn, test_loader)
accuracy = mean(accuracies)
print('Epoch: {}, Mean Accuracy: {:.2f}%'.format(epoch, accuracy))
global_step = (epoch - 1) * num_training_steps_per_epoch
test_utils.add_scalar_to_summary(writer, 'Accuracy/test', accuracy,
global_step)
if FLAGS.metrics_debug:
print(met.metrics_report())
test_utils.close_summary_writer(writer)
return accuracy
def train_imagenet():
print("==> Preparing data..")
img_dim = get_model_property("img_dim")
if FLAGS.fake_data:
train_dataset_len = 1200000 # Roughly the size of Imagenet dataset.
train_loader = xu.SampleGenerator(
data=(
torch.zeros(FLAGS.batch_size, 3, img_dim, img_dim),
torch.zeros(FLAGS.batch_size, dtype=torch.int64),
),
sample_count=train_dataset_len // FLAGS.batch_size //
xm.xrt_world_size(),
)
test_loader = xu.SampleGenerator(
data=(
torch.zeros(FLAGS.test_set_batch_size, 3, img_dim, img_dim),
torch.zeros(FLAGS.test_set_batch_size, dtype=torch.int64),
),
sample_count=50000 // FLAGS.batch_size // xm.xrt_world_size(),
)
else:
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = torchvision.datasets.ImageFolder(
os.path.join(FLAGS.datadir, "train"),
transforms.Compose([
transforms.RandomResizedCrop(img_dim),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]),
)
train_dataset_len = len(train_dataset.imgs)
resize_dim = max(img_dim, 256)
test_dataset = torchvision.datasets.ImageFolder(
os.path.join(FLAGS.datadir, "val"),
# Matches Torchvision's eval transforms except Torchvision uses size
# 256 resize for all models both here and in the train loader. Their
# version crashes during training on 299x299 images, e.g. inception.
transforms.Compose([
transforms.Resize(resize_dim),
transforms.CenterCrop(img_dim),
transforms.ToTensor(),
normalize,
]),
)
train_sampler = None
if xm.xrt_world_size() > 1:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=xm.xrt_world_size(),
rank=xm.get_ordinal(),
shuffle=True,
)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=FLAGS.batch_size,
sampler=train_sampler,
shuffle=False if train_sampler else True,
num_workers=FLAGS.num_workers,
)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=FLAGS.test_set_batch_size,
shuffle=False,
num_workers=FLAGS.num_workers,
)
torch.manual_seed(42)
device = xm.xla_device()
model = get_model_property("model_fn")()
writer = None
if FLAGS.logdir and xm.is_master_ordinal():
writer = SummaryWriter(log_dir=FLAGS.logdir)
optimizer = optim.SGD(model.parameters(),
lr=FLAGS.lr,
momentum=FLAGS.momentum,
weight_decay=1e-4)
num_training_steps_per_epoch = train_dataset_len // (FLAGS.batch_size *
xm.xrt_world_size())
lr_scheduler = schedulers.wrap_optimizer_with_scheduler(
optimizer,
scheduler_type=getattr(FLAGS, "lr_scheduler_type", None),
scheduler_divisor=getattr(FLAGS, "lr_scheduler_divisor", None),
scheduler_divide_every_n_epochs=getattr(
FLAGS, "lr_scheduler_divide_every_n_epochs", None),
num_steps_per_epoch=num_training_steps_per_epoch,
summary_writer=writer,
)
start_epoch = 0
if FLAGS.warm_start:
checkpoint = torch.load(f"./reports/resnet152_model-26.pt")
model.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
lr_scheduler._step_count = checkpoint["step"]
start_epoch = checkpoint["epoch"]
model.to(device)
loss_fn = nn.CrossEntropyLoss()
def train_loop_fn(loader):
tracker = xm.RateTracker()
model.train()
total_samples = 0
correct = 0
top5_accuracys = 0
losses = 0
for x, (data, target) in enumerate(loader):
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss.backward()
xm.optimizer_step(optimizer)
tracker.add(FLAGS.batch_size)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
losses += loss.item()
total_samples += data.size()[0]
top5_accuracys += topk_accuracy(output, target, topk=5).item()
if lr_scheduler:
lr_scheduler.step()
if x % FLAGS.log_steps == 0:
test_utils.print_training_update(device, x, loss.item(),
tracker.rate(),
tracker.global_rate())
return (
losses / (x + 1),
(100.0 * correct / total_samples),
(top5_accuracys / (x + 1)),
)
def test_loop_fn(loader):
total_samples = 0
correct = 0
top5_accuracys = 0
model.eval()
for x, (data, target) in enumerate(loader):
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
top5_accuracys += topk_accuracy(output, target, topk=5).item()
accuracy = 100.0 * correct / total_samples
test_utils.print_test_update(device, accuracy)
return accuracy, top5_accuracys / (x + 1)
accuracy = 0.0
max_accuracy = 0.0
start = time.time()
for epoch in range(start_epoch, FLAGS.num_epochs + 1):
epoch_start = time.time()
para_loader = pl.ParallelLoader(train_loader, [device],
loader_prefetch_size=32,
device_prefetch_size=8)
loss, accuracy, top5_accuracy = train_loop_fn(
para_loader.per_device_loader(device))
if xm.is_master_ordinal():
print(
"Finished training epoch {}, duration_time {} sec, total duration_time {} sec"
.format(epoch,
time.time() - epoch_start,
time.time() - start))
print(
"Epoch: {} (Train), Loss {}, Top-1 Accuracy: {:.2f} Top-5 accuracy: {}"
.format(epoch, loss, accuracy, top5_accuracy))
test_utils.add_scalar_to_summary(writer, "Loss/train", loss, epoch)
test_utils.add_scalar_to_summary(writer, "Top-1 Accuracy/train",
accuracy, epoch)
test_utils.add_scalar_to_summary(writer, "Top-5 Accuracy/train",
top5_accuracy, epoch)
para_loader = pl.ParallelLoader(test_loader, [device])
accuracy, top5_accuracy = test_loop_fn(
para_loader.per_device_loader(device))
if xm.is_master_ordinal():
print(
"Epoch: {} (Valid), Top-1 Accuracy: {:.2f} Top-5 accuracy: {}".
format(epoch, accuracy, top5_accuracy))
test_utils.add_scalar_to_summary(writer, "Top-1 Accuracy/test",
accuracy, epoch)
test_utils.add_scalar_to_summary(writer, "Top-5 Accuracy/test",
top5_accuracy, epoch)
if FLAGS.metrics_debug:
print(met.metrics_report())
if accuracy > max_accuracy:
max_accuracy = max(accuracy, max_accuracy)
xm.save(
{
"epoch": epoch,
"step": lr_scheduler._step_count,
"state_dict": model.state_dict(),
"optimizer": optimizer.state_dict(),
},
f"./reports/{FLAGS.model}_model-{epoch}.pt",
master_only=True,
)
if writer is not None:
writer.flush()
return accuracy
def train_imagenet():
print('==> Preparing data..')
img_dim = get_model_property('img_dim')
if FLAGS.fake_data:
train_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 3, img_dim, img_dim),
torch.zeros(FLAGS.batch_size, dtype=torch.int64)),
sample_count=1200000 // FLAGS.batch_size)
test_loader = xu.SampleGenerator(
data=(torch.zeros(FLAGS.batch_size, 3, img_dim, img_dim),
torch.zeros(FLAGS.batch_size, dtype=torch.int64)),
sample_count=50000 // FLAGS.batch_size)
else:
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = torchvision.datasets.ImageFolder(
os.path.join(FLAGS.datadir, 'train'),
transforms.Compose([
transforms.RandomResizedCrop(img_dim),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=FLAGS.batch_size,
shuffle=True,
num_workers=FLAGS.num_workers)
resize_dim = max(img_dim, 256)
test_dataset = torchvision.datasets.ImageFolder(
os.path.join(FLAGS.datadir, 'val'),
# Matches Torchvision's eval transforms except Torchvision uses size
# 256 resize for all models both here and in the train loader. Their
# version crashes during training on 299x299 images, e.g. inception.
transforms.Compose([
transforms.Resize(resize_dim),
transforms.CenterCrop(img_dim),
transforms.ToTensor(),
normalize,
]))
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=FLAGS.test_set_batch_size,
shuffle=False,
num_workers=FLAGS.num_workers)
torch.manual_seed(42)
devices = (xm.get_xla_supported_devices(
max_devices=FLAGS.num_cores) if FLAGS.num_cores != 0 else [])
# Pass [] as device_ids to run using the PyTorch/CPU engine.
torchvision_model = get_model_property('model_fn')
model_parallel = dp.DataParallel(torchvision_model, device_ids=devices)
def train_loop_fn(model, loader, device, context):
loss_fn = nn.CrossEntropyLoss()
optimizer = context.getattr_or(
'optimizer', lambda: optim.SGD(model.parameters(),
lr=FLAGS.lr,
momentum=FLAGS.momentum,
weight_decay=5e-4))
tracker = xm.RateTracker()
model.train()
for x, (data, target) in loader:
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss.backward()
xm.optimizer_step(optimizer)
tracker.add(FLAGS.batch_size)
if x % FLAGS.log_steps == 0:
print('[{}]({}) Loss={:.5f} Rate={:.2f}'.format(
device, x, loss.item(), tracker.rate()))
def test_loop_fn(model, loader, device, context):
total_samples = 0
correct = 0
model.eval()
for x, (data, target) in loader:
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
print('[{}] Accuracy={:.2f}%'.format(device,
100.0 * correct / total_samples))
return correct / total_samples
accuracy = 0.0
writer = SummaryWriter(log_dir=FLAGS.logdir) if FLAGS.logdir else None
for epoch in range(1, FLAGS.num_epochs + 1):
model_parallel(train_loop_fn, train_loader)
accuracies = model_parallel(test_loop_fn, test_loader)
accuracy = mean(accuracies)
print("Epoch: {}, Mean Accuracy: {:.2f}%".format(epoch, accuracy))
test_utils.add_scalar_to_summary(writer, 'Accuracy/test', accuracy,
epoch)
if FLAGS.metrics_debug:
print(torch_xla._XLAC._xla_metrics_report())
return accuracy * 100.0
